Systems and methods for maternal uterine activity detection

ABSTRACT

A method includes receiving bio-potential inputs; generating signal channels from the bio-potential inputs; pre-processing data in the signal channels; extracting R-wave peaks from the pre-processed data; removing artifacts and outliers from the R-wave peaks; generating R-wave signal channels based on the R-wave peaks in the pre-processed signal channels; selecting two or more of the R-wave signal channels; and combining the selected two or more R-wave signal channels to produce an electrical uterine monitoring signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/529,696, filed Aug. 1, 2019, entitled SYSTEMS AND METHODS FORMATERNAL UTERINE ACTIVITY DETECTION, which is a Section 111(a)application relating to and claiming the benefit of commonly-owned,co-pending U.S. Provisional Patent Application No. 62/713,324, filedAug. 1, 2018, entitled SYSTEMS AND METHODS FOR MATERNAL UTERINE ACTIVITYDETECTION, and U.S. Provisional Patent Application No. 62/751,011, filedOct. 26, 2018, entitled SYSTEMS AND METHODS FOR MATERNAL UTERINEACTIVITY DETECTION, the contents of which are incorporated herein byreference in their entirety.

FIELD OF THE INVENTION

The invention relates generally to monitoring of expectant mothers. Moreparticularly, the invention relates to analysis of sensed bio-potentialdata to produce computed representations of uterine activity, such asuterine contractions.

BACKGROUND

A uterine contraction is a temporary process during which the muscles ofthe uterus are shortened and the space between muscle cells decreases.These structural changes in the muscle cause an increase in uterinecavity pressure to allow pushing the fetus downward in a lower positiontowards delivery. During a uterine contraction, the structure ofmyometrial cells (i.e., cells of the uterus) changes and the uterinewall becomes thicker. FIG. 1A shows an illustration of a relaxed uterus,in which the muscular wall of the uterus is relaxed. FIG. 1B shows anillustration of a contracted uterus, in which the muscular wall of theuterus contracts and pushes the fetus against the cervix.

Uterine contractions are monitored to evaluate the progress of labor.Typically, the progress of labor is monitored through the use of twosensors: a tocodynamometer, which is a strain gauge-based sensorpositioned on the abdomen of an expectant mother, and an ultrasoundtransducer, which is also positioned on the abdomen. The signals of thetocodynamometer are used to provide a tocograph (“TOCO”), which isanalyzed to identify uterine contractions, while the signals of theultrasound transducer are used to detect fetal heart rate, maternalheart rate, and fetal movement. However, these sensors can beuncomfortable to wear, and can produce unreliable data when worn byobese expectant mothers.

SUMMARY

In some embodiments, the present invention provides a specificallyprogrammed computer system, including at least the following components:a non-transient memory, electronically storing computer-executableprogram code; and at least one computer processor that, when executingthe program code, becomes a specifically programmed computing processorthat is configured to perform at least the following operations:receiving a plurality of bio-potential signals collected at a pluralityof locations on the abdomen of a pregnant mother; detecting R-wave peaksin the bio-potential signals; extracting maternal electrocardiogram(“ECG”) signals from the bio-potential signals; determining R-waveamplitudes in the maternal ECG signals; creating an R-wave amplitudesignal for each of the maternal ECG signals; calculating an average ofall the R-wave amplitude signals; and normalizing the average to producean electrical uterine monitoring (“EUM”) signal. In some embodiments,the operations also include identifying at least one uterine contractionbased on a corresponding at least one peak in the EUM signal.

In some embodiments, the present invention provides a method includingreceiving a plurality of bio-potential signals collected at a pluralityof locations on the abdomen of a pregnant mother; detecting R-wave peaksin the bio-potential signals; extracting maternal ECG signals from thebio-potential signals; determining R-wave amplitudes in the maternal ECGsignals; creating an R-wave amplitude signal for each of the maternalECG signals; calculating an average of all the R-wave amplitude signals;and normalizing the average to produce an EUM signal. In someembodiments, the method also includes identifying at least one uterinecontraction based on a corresponding at least one peak in the EUMsignal.

In an embodiment, a computer-implemented method receiving, by at leastone computer processor, a plurality of raw bio-potential inputs, whereineach of the raw bio-potential inputs being received from a correspondingone of a plurality of electrodes, wherein each of the plurality ofelectrodes is positioned so as to measure a respective one of the rawbio-potential inputs of a pregnant human subject; generating, by the atleast one computer processor, a plurality of signal channels from theplurality of raw-bio-potential inputs, wherein the plurality of signalchannels comprises at least three signal channels; pre-processing, bythe at least one computer processor, respective signal channel data ofeach of the signal channels to produce a plurality of pre-processedsignal channels, wherein each of the pre-processed signal channelscomprises respective pre-processed signal channel data; extracting, bythe at least one computer processor, a respective plurality of R-wavepeaks from the pre-processed signal channel data of each of thepre-processed signal channels to produce a plurality of R-wave peak datasets, wherein each of the R-wave peak data sets comprises a respectiveplurality of R-wave peaks; removing, by the at least one computerprocessor, from the plurality of R-wave peak data sets, at least one of:(a) at least one signal artifact or (b) at least one outlier data point,wherein the at least one signal artifact is one of an electromyographyartifact or a baseline artifact; replacing, by the at least one computerprocessor, the at least one signal artifact, the at least one outlierdata point, or both, with at least one statistical value determinedbased on a corresponding one of the R-wave peak data sets from which theat least one signal artifact, the at least one outlier data point, orboth was removed; generating, by the at least one computer processor, arespective R-wave signal data set for a respective R-wave signal channelat a predetermined sampling rate based on each respective R-wave peakdata set to produce a plurality of R-wave signal channels; selecting, bythe at least one computer processor, at least one first selected R-wavesignal channel and at least one second selected R-wave signal channelfrom the plurality of R-wave channels based on at least one correlationbetween (a) the respective R-wave signal data set of at least one firstparticular R-wave signal channel and (b) the respective R-wave signaldata set of at least one second particular R-wave signal channel;generating, by the at least one computer processor, electrical uterinemonitoring data representative of an electrical uterine monitoringsignal based on at least the respective R-wave signal data set of thefirst selected R-wave signal channel and the respective R-wave signaldata set of the second selected R-wave signal channel.

In an embodiment, a computer-implemented method also includessharpening, by the at least one computer processor, the electricaluterine monitoring data to produce a sharpened electrical uterinemonitoring signal. In an embodiment, the sharpening step is omitted ifthe electrical uterine monitoring data is calculated based on a selectedone of the electrical uterine monitoring signal channels that is acorrupted electrical uterine signal monitoring channel. In anembodiment, a computer-implemented method also includes post-processingthe sharpened electrical monitoring signal data to produce apost-processed electrical uterine monitoring signal. In an embodiment,the sharpening step includes identifying a set of peaks in theelectrical uterine monitoring signal data; determining a prominence ofeach of the peaks; removing, from the set of peaks, peaks having aprominence that is less than at least one threshold prominence value;calculating a mask based on remaining peaks of the set of peaks;smoothing the mask based on a moving average window to produce asmoothed mask; and adding the smoothed mask to the electrical uterinemonitoring signal data to produce the sharpened electrical uterinemonitoring signal data. In an embodiment, the at least one thresholdprominence value includes at least one threshold prominence valueselected from the group consisting of an absolute prominence value and arelative prominence value calculated based on a maximal prominence ofthe peaks in the set of peaks. In an embodiment, the mask includes zerovalues outside areas of the remaining peaks and nonzero values insideareas of the remaining peaks, wherein the nonzero values are calculatedbased on a Gaussian function

In an embodiment, the at least one filtering step of the pre-processingstep includes applying at least one filter selected from the groupconsisting of a DC removal filter, a powerline filter, and a high passfilter.

In an embodiment, the extracting step comprises receiving a set ofmaternal ECG peaks for the pregnant human subject; and identifyingR-wave peaks in each of the pre-processed signal channels within apredetermined time window before and after each of the maternal ECGpeaks in the set of maternal ECG peaks as the maximum absolute value ineach of the pre-processed signal channels within the predetermined timewindow.

In an embodiment, the step of removing at least one of a signal artifactor an outlier data point includes removing at least one electromyographyartifact by a process including identifying at least one corrupted peakin one of the plurality of R-wave peaks data sets based on the at leastone corrupted peak having an inter-peaks root mean square value that isgreater than a threshold; and replacing the corrupted peak with a medianvalue, wherein the median value is either a local median or a globalmedian.

In an embodiment, the step of removing at least one of a signal artifactor an outlier data point comprises removing at least one baselineartifact by a process including: identifying a change point in R-wavepeaks in one of the plurality of R-wave peaks data sets; subdividing theone of the plurality of R-wave peaks data sets into a first portionlocated prior to the change point and a second portion locatedsubsequent to the change point; determining a first root-mean-squarevalue for the first portion; determining a second root-mean-square valuefor the second portion; determining an equalization factor based on thefirst root-mean-square value and the second root-mean-square value; andmodifying the first portion by multiplying R-wave peaks in the firstportion by the equalization factor.

In an embodiment, the step of removing at least one of a signal artifactor an outlier point comprises removing at least one outlier inaccordance with a Grubbs test for outliers.

In an embodiment, the step of generating a respective R-wave data setbased on each respective R-wave peak data set comprises interpolatingbetween the R-wave peaks of each respective R-wave peak data set, andwherein the interpolating between the R-wave peaks comprisesinterpolating using an interpolation algorithm that is selected from thegroup consisting of a cubic spline interpolation algorithm and ashape-preserving piecewise cubic interpolation algorithm.

In an embodiment, the step of selecting at least one first one of theR-wave signal channels and at least one second one of the R-wave signalchannels includes selecting candidate R-wave signal channels from theR-wave signal channels based on a percentage of prior intervals in whicheach of the R-wave signal channels experienced contact issues; groupingthe selected candidate R-wave signal channels into a plurality ofcouples, wherein each of the couples includes two of the selectedcandidate R-wave channels that are independent from one another;calculating a correlation value of each of the couples; and selecting,as the selected at least one first one of the R-wave signal channels andthe selected at least one second one of the R-wave signal channels, thecandidate R-wave signal channels of at least one of the couples based onthe at least one of the couples having a correlation value that exceedsa threshold correlation value.

In an embodiment, the step of calculating the electrical uterinemonitoring signal comprises calculating a signal that is a predeterminedpercentile of the selected at least one first one of the R-wave signalchannels and the selected at least one second one of the R-wave signalchannels. In an embodiment, the predetermined percentile is an 80^(th)percentile.

In an embodiment, the statistical value is one of a local median, aglobal median, or a mean.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows a representative uterus in a non-contracted state.

FIG. 1B shows a representative uterus in a contracted state.

FIG. 2 shows a flowchart of a first exemplary method.

FIG. 3 shows an exemplary garment including a plurality of bio-potentialsensors, which may be used to sense data that is to be analyzed inaccordance with the exemplary method of FIG. 2.

FIG. 4A shows a front view of the positions of ECG sensor pairs on theabdomen of a pregnant woman according to some embodiments of the presentinvention.

FIG. 4B shows a side view of the positions of the ECG sensor pairs onthe abdomen of a pregnant woman according to some embodiments of thepresent invention.

FIG. 5 shows an exemplary bio-potential signal before and afterpre-processing.

FIG. 6A shows an exemplary bio-potential signal after pre-processing andwith detected R-wave peaks indicated.

FIG. 6B shows the exemplary bio-potential signal of FIG. 6A after peakre-detection.

FIG. 6C shows a magnified view of a portion of the signal of FIG. 6B.

FIG. 6D shows the exemplary bio-potential signal of FIG. 6B afterexamination of the detected peaks.

FIG. 7A shows a portion of an exemplary bio-potential signal includingidentified R-wave peaks.

FIG. 7B shows a portion of an exemplary bio-potential signal having aP-wave, a QRS complex, and a T-wave identified therein.

FIG. 7C shows an exemplary bio-potential signal including mixed maternaland fetal data.

FIG. 7D shows a portion of the signal of FIG. 7C along with an initialtemplate.

FIG. 7E shows a portion of the signal of FIG. 7C along with an adaptedtemplate.

FIG. 7F shows a portion of the signal of FIG. 7C along with a currenttemplate and a zero-th iteration of an adaptation.

FIG. 7G shows a portion of the signal of FIG. 7C along with a currenttemplate, a zero-th iteration of an adaptation, and a first iteration ofan adaptation.

FIG. 7H shows a portion of the signal of FIG. 7C along with a currenttemplate and a maternal ECG signal reconstructed based on the currenttemplate.

FIG. 7I shows the progress of an adaptation expressed in terms of thelogarithm of the error signal as plotted against the number of theiteration.

FIG. 7J shows an extracted maternal ECG signal.

FIG. 8 shows an exemplary filtered maternal ECG signal.

FIG. 9 shows an exemplary maternal ECG signal with R-wave peaksannotated therein.

FIG. 10A shows an exemplary R-wave amplitude signal.

FIG. 10B shows an exemplary modulated R-wave amplitude signal.

FIG. 11A shows an exemplary modulated R-wave amplitude signal along withthe result of the application of a moving average filter thereto.

FIG. 11B shows exemplary filtered R-wave amplitude signals for multiplechannels over a same time window.

FIG. 12A shows a first exemplary normalized electrical uterine signalgenerated based on the exemplary filtered R-wave amplitude signals shownin FIG. 11B.

FIG. 12B shows a first tocograph signal recorded over the same timeperiod as the exemplary normalized electrical uterine signal withself-reported contractions annotated therein.

FIG. 13 shows a flowchart of a second exemplary method.

FIG. 14A shows a second tocograph signal with self-reported contractionsannotated therein.

FIG. 14B shows a second exemplary electrical uterine signal derived frombio-potential data recorded during the same time period as that shown inFIG. 14A.

FIG. 15A shows a third tocograph signal with self-reported contractionsannotated therein.

FIG. 15B shows a third exemplary electrical uterine signal derived frombio-potential data recorded during the same time period as that shown inFIG. 15A.

FIG. 16A shows a fourth tocograph signal with self-reported contractionsannotated therein.

FIG. 16B shows a fourth exemplary electrical uterine signal derived frombio-potential data recorded during the same time period as that shown inFIG. 16A.

FIG. 17A shows a fifth tocograph signal with self-reported contractionsannotated therein.

FIG. 17B shows a fifth exemplary electrical uterine signal derived frombio-potential data recorded during the same time period as that shown inFIG. 17A.

FIG. 18A shows an exemplary raw bio-potential data set.

FIG. 18B shows an exemplary filtered data set based on the exemplary rawdata set of FIG. 18A.

FIG. 18C shows an exemplary raw bio-potential data set.

FIG. 18D shows an exemplary filtered data set based on the exemplary rawdata set of FIG. 18C.

FIG. 18E shows an exemplary raw bio-potential data set.

FIG. 18F shows an exemplary filtered data set based on the exemplary rawdata set of FIG. 18E.

FIG. 18G shows an exemplary raw bio-potential data set.

FIG. 18H shows an exemplary filtered data set based on the exemplary rawdata set of FIG. 18G.

FIG. 19A shows an exemplary filtered data set with input peak positions.

FIG. 19B shows the exemplary filtered data set of FIG. 19A withextracted peak positions.

FIG. 20A shows an exemplary filtered data set.

FIG. 20B shows the exemplary filtered data set of FIG. 20A withrepresentations of a corresponding maternal motion envelope and aninter-peaks absolute sum.

FIG. 20C shows an exemplary corrected data set produced by removal ofelectromyography artifacts from the filtered data set of FIG. 20A.

FIG. 21A shows an exemplary corrected data set including a baselineartifact.

FIG. 21B shows the exemplary corrected data set of FIG. 21A followingremoval of the baseline artifact.

FIG. 22A shows an exemplary corrected data set including an outlier datapoint.

FIG. 22B shows the exemplary corrected data set of FIG. 22A followingremoval of the outlier data point.

FIG. 23A shows an exemplary R-wave peaks signal.

FIG. 23B shows an exemplary R-wave signal generated based on theexemplary R-wave peaks signal of FIG. 23A.

FIG. 24A shows an exemplary set of candidate R-wave signal channels.

FIG. 24B shows an exemplary set of selected signal channels based on theexemplary set of candidate R-wave signal channels of FIG. 24A.

FIG. 25A shows an exemplary electrical uterine monitoring signalgenerated based on the set of selected signal channels shown in FIG.24B.

FIG. 25B shows an exemplary corrected electrical uterine monitoringsignal generated by applying wandering baseline removal to the exemplaryelectrical uterine monitoring signal of FIG. 25A.

FIG. 26 shows an exemplary normalized electrical uterine monitoringsignal generated based on the exemplary corrected electrical uterinemonitoring signal of FIG. 25B.

FIG. 27A shows an exemplary normalized electrical uterine monitoringsignal.

FIG. 27B shows an exemplary sharpening mask generated based on theexemplary normalized electrical uterine monitoring signal of FIG. 27A.

FIG. 27C shows an exemplary sharpened electrical uterine monitoringsignal generated based on the exemplary normalized electrical uterinemonitoring signal of FIG. 27A and the exemplary sharpening mask of FIG.27B.

FIG. 28 shows an exemplary post-processed electrical uterine monitoringsignal.

FIG. 29 shows a tocograph signal corresponding to the exemplarypost-processed electrical uterine monitoring signal of FIG. 29.

DETAILED DESCRIPTION

Among those benefits and improvements that have been disclosed, otherobjects and advantages of this invention will become apparent from thefollowing description taken in conjunction with the accompanyingfigures. Detailed embodiments of the present invention are disclosedherein; however, it is to be understood that the disclosed embodimentsare merely illustrative of the invention that may be embodied in variousforms. In addition, each of the examples given in connection with thevarious embodiments of the invention which are intended to beillustrative, and not restrictive.

Throughout the specification and claims, the following terms take themeanings explicitly associated herein, unless the context clearlydictates otherwise. The phrases “in one embodiment,” “in an embodiment,”and “in some embodiments” as used herein do not necessarily refer to thesame embodiment(s), though it may. Furthermore, the phrases “in anotherembodiment” and “in some other embodiments” as used herein do notnecessarily refer to a different embodiment, although it may. Thus, asdescribed below, various embodiments of the invention may be readilycombined, without departing from the scope or spirit of the invention.

As used herein, the term “based on” is not exclusive and allows forbeing based on additional factors not described, unless the contextclearly dictates otherwise. In addition, throughout the specification,the meaning of “a,” “an,” and “the” include plural references. Themeaning of “in” includes “in” and “on.” Ranges discussed herein areinclusive (e.g., a range of “between 0 and 2” includes the values 0 and2 as well as all values therebetween).

As used herein the term “contact region” encompasses the contact areabetween the skin of a pregnant human subject and cutaneous contact i.e.the surface area through which current flow can pass between the skin ofthe pregnant human subject and the cutaneous contact.

In some embodiments, the present invention provides a method forextracting a tocograph-like signal from bio-potential data, that is,data describing electrical potential recorded at points on a person'sskin through the use of cutaneous contacts, commonly called electrodes.In some embodiments, the present invention provides a method fordetecting uterine contractions from bio-potential data. In someembodiments, bio-potential data is obtained through the use ofnon-contact electrodes positioned against or in the vicinity of desiredpoints on a person's body.

In some embodiments, the present invention provides a system fordetecting, recording and analyzing cardiac electrical activity data froma pregnant human subject. In some embodiments, a plurality of electrodesconfigured to detect fetal electrocardiogram signals is used to recordthe cardiac activity data. In some embodiments, a plurality ofelectrodes configured to detect fetal electrocardiogram signals and aplurality of acoustic sensors are used to record the cardiac activitydata.

In some embodiments, a plurality of electrodes configured to detectfetal electrocardiogram signals are attached to the abdomen of thepregnant human subject. In some embodiments, the plurality of electrodesconfigured to detect fetal electrocardiogram signals are directlyattached to the abdomen. In some embodiments, the plurality ofelectrodes configured to detect fetal electrocardiogram signals areincorporated into an article, such as, for example, a belt, a patch, andthe like, and the article is worn by, or placed on, the pregnant humansubject. FIG. 3 shows an exemplary garment 300, which includes eightelectrodes 310 incorporated into the garment 300 so as to be positionedaround the abdomen of a pregnant human subject when the garment 300 isworn by the subject. FIG. 4A shows a front view of the positions of theeight electrodes 310 on the abdomen of a pregnant woman according tosome embodiments of the present invention. FIG. 4B shows a side view ofthe eight electrodes 310 on the abdomen of a pregnant woman according tosome embodiments of the present invention.

FIG. 2 shows a flowchart of a first exemplary inventive method 200. Insome embodiments, an exemplary inventive computing device,programmed/configured in accordance with the method 200, is operative toreceive, as input, raw bio-potential data measured by a plurality ofelectrodes positioned on the skin of a pregnant human subject, andanalyze such input to produce a tocograph-like signal. In someembodiments, the quantity of electrodes is between 2 and 10. In someembodiments, the quantity of electrodes is between 2 and 20. In someembodiments, the quantity of electrodes is between 2 and 30. In someembodiments, the quantity of electrodes is between 2 and 40. In someembodiments, the quantity of electrodes is between 4 and 10. In someembodiments, the quantity of electrodes is between 4 and 20. In someembodiments, the quantity of electrodes is between 4 and 30. In someembodiments, the quantity of electrodes is between 4 and 40. In someembodiments, the quantity of electrodes is between 6 and 10. In someembodiments, the quantity of electrodes is between 6 and 20. In someembodiments, the quantity of electrodes is between 6 and 30. In someembodiments, the quantity of electrodes is between 6 and 40. In someembodiments, the quantity of electrodes is between 8 and 10. In someembodiments, the quantity of electrodes is between 8 and 20. In someembodiments, the quantity of electrodes is between 8 and 30. In someembodiments, the quantity of electrodes is between 8 and 40. In someembodiments, the quantity of electrodes is 8. In some embodiments, theexemplary inventive computing device, programmed/configured inaccordance with the method 200, is operative to receive, as input,maternal ECG signals that have already been extracted from rawbio-potential data (e.g., by separation from fetal ECG signals that formpart of the same raw bio-potential data). In some embodiments, theexemplary inventive computing device is programmed/configured inaccordance with the method 200 via instructions stored in anon-transitory computer-readable medium. In some embodiments, theexemplary inventive computing device includes at least one computerprocessor, which, when executing the instructions, becomes aspecifically-programmed computer processor programmed/configured inaccordance with the method 200.

In some embodiments, the exemplary inventive computing device isprogrammed/configured to continuously perform one or more steps of themethod 200 along a moving time window. In some embodiments, the movingtime window has a predefined length. In some embodiments, the predefinedlength is sixty seconds. In some embodiments, the exemplary inventivecomputing device is programmed/configured to continuously perform one ormore steps of the method 200 along a moving time window having a lengththat is between one second and one hour. In some embodiments, the lengthof the moving time window is between thirty seconds and 30 minutes. Insome embodiments, the length of the moving time window is between 30seconds and 10 minutes. In some embodiments, the length of the movingtime window is between 30 seconds and 5 minutes. In some embodiments,the length of the moving time window is about 60 seconds. In someembodiments, the length of the moving time window is 60 seconds.

In step 210, the exemplary inventive computing device isprogrammed/configured to receive raw bio-potential data as input andpre-process it. In some embodiments, the raw bio-potential data isrecorded through the use of at least two electrodes positioned inproximity to a pregnant subject's skin. In some embodiments, at leastone of the electrodes is a signal electrode. In some embodiments atleast one of the electrodes is a reference electrode. In someembodiments, the reference electrode is located at a point away from theuterus of the subject. In some embodiments, a bio-potential signal isrecorded at each of several points around the pregnant subject'sabdomen. In some embodiments, a bio-potential signal is recorded at eachof eight points around the pregnant subject's abdomen. In someembodiments, the bio-potential data is recorded at 1,000 samples persecond. In some embodiments, the bio-potential data is up-sampled to1,000 samples per second. In some embodiments, the bio-potential data isrecorded at a sampling rate of between 100 and 10,000 samples persecond. In some embodiments, the bio-potential data is up-sampled to asampling rate of between 100 and 10,000 samples per second. In someembodiments, the pre-processing includes baseline removal (e.g., using amedian filter and/or a moving average filter). In some embodiments, thepre-processing includes low-pass filtering. In some embodiments, thepre-processing includes low-pass filtering at 85 Hz. In someembodiments, the pre-processing includes power line interferencecancellation. FIG. 5 shows a portion of a raw bio-potential data signalboth before and after pre-processing.

In step 220, the exemplary inventive computing device isprogrammed/configured to detect maternal R-wave peaks in thepre-processed bio-potential data resulting from the performance of step210. In some embodiments, R-wave peaks are detected over 10-secondsegments of each data signal. In some embodiments, the detection ofR-wave peaks begins by analysis of derivatives, thresholding, anddistances. In some embodiments, the detection of R-wave peaks in eachdata signal includes calculating the first derivative of the data signalin the 10-second segment, identifying an R-wave peak in the 10-secondsegment by identifying a zero-crossing of the first derivative, andexcluding identified peaks having either (a) an absolute value that isless than a predetermined R-wave peak threshold absolute value or (b) adistance between adjacent identified R-wave peaks that is less than apredetermined R-wave peak threshold distance. In some embodiments, thedetection of R-wave peaks is performed in a manner similar to thedetection of electrocardiogram peaks described in U.S. Pat. No.9,392,952, the contents of which are incorporated herein by reference intheir entirety. FIG. 6A shows a pre-processed bio-potential data signal,with R-wave peaks detected as described above indicated with asterisks.

In some embodiments, the detection of R-wave peaks of step 220 continueswith a peak re-detection process. In some embodiments, the peakre-detection process includes an automatic gain control (“AGC”) analysisto detect windows with significantly different numbers of peaks. In someembodiments, the peak re-detection process includes a cross-correlationanalysis. In some embodiments, the peak re-detection process includes anAGC analysis and a cross-correlation analysis. In some embodiments, anAGC analysis is appropriate for overcoming false negatives. In someembodiments, a cross-correlation analysis is appropriate for removingfalse positives. FIG. 6B shows a data signal following peakre-detection, with R-wave peaks re-detected as described above indicatedwith asterisks. FIG. 6C shows a magnified view of a portion of the datasignal of FIG. 6B.

In some embodiments, the detection of R-wave peaks of step 220 continueswith the construction of a global peaks array. In some embodiments, theglobal peaks array is created from multiple channels of data (e.g., eachof which corresponds to one or more of the electrodes 310). In someembodiments, the signal of each channel is given a quality score basedon the relative energy of the peaks. In some embodiments, the relativeenergy of a peak refers to the energy of the peak relative to the totalenergy of the signal under processing. In some embodiments, the energyof a peak is calculated by calculating a root mean square (“RMS”) of theQRS complex containing the R-wave peak and the energy of a signal iscalculated by calculating the RMS of the signal. In some embodiments,the relative energy of a peak is calculated by calculating asignal-to-noise ratio of the signal. In some embodiments, the channelhaving the highest quality score is deemed the “Best Lead”. In someembodiments, the global peaks array is constructed based on the BestLead, with signals from the other channels also considered based on avoting mechanism. In some embodiments, after the global peaks array hasbeen constructed based on the Best Lead, each of the remaining channels“votes” on each peak. A channel votes positively (e.g., gives a votevalue of “1”) on a given peak that is included in the global peaks arrayconstructed based on the best lead if it contains such peak (e.g., asdetected in the peak detection described above), and votes negatively(e.g., gives a vote value of “0”) if it does not contain such peak.Peaks that receive more votes are considered to be higher-quality peaks.In some embodiments, if a peak has greater than a threshold number ofvotes, it is retained in the global peaks array. In some embodiments,the threshold number of votes is half of the total number of channels.In some embodiments, if a peak has less than the threshold number ofvotes, additional testing is performed on the peak. In some embodiments,the additional testing includes calculating a correlation of the peak inthe Best Lead channel with a template calculated as the average of allpeaks. In some embodiments, if the correlation is greater than a firstthreshold correlation value, the peak is retained in the global peaksarray. In some embodiments, the first threshold correlation value is0.9. In some embodiments, if the correlation is less than the firstthreshold correlation value, a further correlation is calculated for allleads with positive votes for the peak (i.e., not just the Best Leadpeak). In some embodiments, if the further correlation is greater than asecond threshold correlation value, the peak is retained in the globalpeaks array, and if the further correlation is less than the secondthreshold correlation value, the peak is excluded from the global peaksarray. In some embodiments, the second threshold correlation value is0.85.

In some embodiments, once created, the global peaks array is examinedusing physiological measures. In some embodiments, the examination isperformed by the exemplary inventive computing device as described inU.S. Pat. No. 9,392,952, the contents of which are incorporated hereinin their entirety. In some embodiments, the physiological parametersinclude R-R intervals, mean, and standard deviation; and heart rate andheart rate variability. In some embodiments, the examination includescross-correlation to overcome false negatives. FIG. 6D shows a datasignal following creation and examination of the global peaks array asdescribed above. In FIG. 6D, peaks denoted by circled asterisksrepresent R-wave peaks that were detected previously (e.g., as shown inFIG. 6A), and circles with no asterisks represent R-wave peaks detectedby cross-correlation to overcome false negatives as described above.

In some embodiments, if an initial step of R-wave detection wasunsuccessful (i.e., if no R-wave peaks were detected over a givensample), an independent component analysis (“ICA”) algorithm is appliedto the data samples and the earlier portions of step 220 are repeated.In some embodiments, the exemplary ICA algorithm is, for example but notlimited to, the FAST ICA algorithm. In some embodiments, the FAST ICAalgorithm is, for example, utilized in accordance with Hyvarinen et al.,“Independent component analysis: Algorithms and applications,” NeuralNetworks 13 (4-5): 411-430 (2000).

Continuing to refer to FIG. 2, in step 230, the exemplary inventivecomputing device is programmed/configured to extract maternal ECGsignals from signals that include both maternal and fetal data. In someembodiments, where the exemplary inventive computing device,programmed/configured to execute the method 200, receives maternal ECGsignals as input after extraction from mixed maternal-fetal data, theexemplary inventive computing device is programmed/configured to skipstep 230. FIG. 7A shows a portion of a signal in which R-wave peaks havebeen identified, and which includes both maternal and fetal signals.Without intending to be limited to any particular theory, the mainchallenge involved in the process of extracting maternal ECG signals isthat each maternal heartbeat differs from all other maternal heartbeats.In some embodiments, this challenge is addressed by using an adaptivereconstruction scheme to identify each maternal heartbeat. In someembodiments, the extraction process begins by segmenting an ECG signalinto a three-sourced signal. In some embodiments, this segmentationincludes using a curve length transform to find a P-wave, a QRS complex,and a T-wave. In some embodiments, the curve length transform is asdescribed in Zong et al., “A QT Interval Detection Algorithm Based OnECG Curve Length Transform,” Computers In Cardiology 33:377-380 (October2006). FIG. 7B shows an exemplary ECG signal including these portions.

Following the curve length transform, step 230 continues by using anadaptive template to extract the maternal signal. In some embodiments,template adaptation is used to isolate the current beat. In someembodiments, the extraction of the maternal signal using an adaptivetemplate is performed as described in U.S. Pat. No. 9,392,952, thecontents of which are incorporated herein by reference in theirentirety. In some embodiments, this process includes beginning with acurrent template and adapting the current template using an iterativeprocess to arrive at the current beat. In some embodiments, for eachpart of the signal (i.e., the P-wave, the QRS complex, and the T-wave),a multiplier is defined (referred to as P_mult, QRS_mult, and T_mult,respectively). In some embodiments, a shifting parameter is alsodefined. In some embodiments, the extraction uses a Levenberg-Marquardtnon-linear least mean squares algorithm, as shown below:

P _(k+1) =P _(k)−[

_(k) ^(T)

_(k) +λ _(i)·diag(

_(k) ^(T)

_(k))]⁻¹ *

_(k) ^(T)[(ϕ_(c)(P_(k))−ϕ_(m)]

In some embodiments, the cost function is as shown below:

E=∥ϕ _(m) −ϕ _(c)∥²

In the above expressions, ϕ_(m) represents the current beat ECG andϕ_(c) represents the reconstructed ECG. In some embodiments, this methodprovides a local, stable, and repeatable solution. In some embodiments,iteration proceeds until the relative remaining energy has reached athreshold value. In some embodiments, the threshold value is between 0db and −40 db. In some embodiments, the threshold value is between −10db and −40 db. In some embodiments, the threshold value is between −20db and −40 db. In some embodiments, the threshold value is between −30db and −40 db. In some embodiments, the threshold value is between −10db and −30 db. In some embodiments, the threshold value is between −10db and −20 db. In some embodiments, the threshold value is between −20db and −40 db. In some embodiments, the threshold value is between −20db and −30 db. In some embodiments, the threshold value is between −30db and −40 db. In some embodiments, the threshold value is between −25db and −35 db. In some embodiments, the threshold value is about −20 db.In some embodiments, the threshold value is about −20 db.

FIG. 7C shows an exemplary signal including mixed maternal and fetaldata. FIG. 7D shows a portion of the signal of FIG. 7C along with aninitial template for comparison. FIG. 7E shows the portion of the signalof FIG. 7C along with an adapted template for comparison. FIG. 7F showsa portion of the signal of FIG. 7C, a current template, and a 0thiteration of the adaptation. FIG. 7G shows a portion of the signal ofFIG. 7C, a current template, a 0th iteration of the adaptation, and a1st iteration of the adaptation. FIG. 7H shows a portion of the signalof FIG. 7C, a current template, and an ECG signal (e.g., a maternal ECGsignal) reconstructed based on the current template. FIG. 7I shows theprogress of the adaptation in terms of the logarithm of the error signalagainst the number of the iteration. FIG. 7J shows an extracted maternalECG signal.

Continuing to refer to FIG. 2, in step 240, the exemplary inventivecomputing device is programmed/configured to perform a signal cleanup onthe maternal signals extracted in step 230. In some embodiments, thecleanup of step 240 includes filtering. In some embodiments, thefiltering includes baseline removal using a moving average filter. Insome embodiments, the filtering includes low pass filtering. In someembodiments, the low pass filtering is performed at between 25 Hz and125 Hz. In some embodiments, the low pass filtering is performed atbetween 50 Hz and 100 Hz. In some embodiments, the low pass filtering isperformed at 75 Hz. FIG. 8 shows a portion of an exemplary filteredmaternal ECG following the performance of step 240.

Continuing to refer to FIG. 2, in step 250, the exemplary inventivecomputing device is programmed/configured to calculate R-wave amplitudesfor the filtered maternal ECG signals resulting from the performance ofstep 240. In some embodiments, R-wave amplitudes are calculated based onthe maternal ECG peaks that were detected in step 220 and the maternalECG signals that were extracted in step 230. In some embodiments, step250 includes calculating the amplitude of the various R-waves. In someembodiments, the amplitude is calculated as the value (e.g., signalamplitude) of the maternal ECG signals at each detected peak position.FIG. 9 shows an exemplary extracted maternal ECG signal with R-wavepeaks annotated with circles.

Continuing to refer to FIG. 2, in step 260, the exemplary inventivecomputing device is programmed/configured to create an R-wave amplitudesignal over time based on the R-wave amplitudes that were calculated instep 250. In some embodiments, the calculated R-wave peaks are notsampled uniformly over time. Accordingly, in some embodiments, step 260is performed in order to re-sample the R-wave amplitudes in a way suchthat they will be uniformly sampled over time (e.g., such that thedifference in time between each two adjacent samples is constant). Insome embodiments, step 260 is performed by connecting the R-waveamplitude values that were calculated in step 250 and re-sampling theconnected R-wave amplitude values. In some embodiments, the re-samplingincludes interpolation with defined query points in time. In someembodiments, the interpolation includes linear interpolation. In someembodiments, the interpolation includes spline interpolation. In someembodiments, the interpolation includes cubic interpolation. In someembodiments, the query points define the points in time at whichinterpolation should occur. FIG. 10A shows an exemplary R-wave amplitudesignal as created in step 260 based on the R-wave amplitudes from step250. In FIG. 10A, the maternal ECG is similar to that shown in FIG. 8,the detected R-wave peaks are shown in circles, and the R-wave amplitudesignal is the curve connecting the circles. FIG. 10B shows themodulation of the R-wave amplitude signal over a larger time window.

Continuing to refer to FIG. 2, in step 270, the exemplary inventivecomputing device is programmed/configured to clean up the R-waveamplitude signal by applying a moving average filter. In someembodiments, the moving average filter is applied to clean thehigh-frequency changes in the R-wave amplitude signal. In someembodiments, the moving average filter is applied over a predeterminedtime window. In some embodiments, the time window has a length ofbetween one second and ten minutes. In some embodiments, the time windowhas a length of between one second and one minute. In some embodiments,the time window has a length of between one second and 30 seconds. Insome embodiments, the time window has a length of twenty seconds. FIG.11A shows the R-wave amplitude signal of FIG. 10B, with the signalresulting from the application of the moving average filter shown in athick line along the middle of the R-wave amplitude signal. As notedabove, in some embodiments, multiple channels of data are considered asinput for the method 200. FIG. 11B shows a plot of filtered R-waveamplitude signals for multiple channels over the same time window.

Continuing to refer to FIG. 2, in step 280, the exemplary inventivecomputing device is programmed/configured to calculate the averagesignal of all the filtered R-wave signals (e.g., as shown in FIG. 11B)per unit of time. In some embodiments, at each point in time for which asample exists, a single average signal is calculated. In someembodiments, the average signal is the 80th percentile of all thesignals at each point in time. In some embodiments, the average signalis the 85th percentile of all the signals at each point in time. In someembodiments, the average signal is the 90th percentile of all thesignals at each point in time. In some embodiments, the average signalis the 95th percentile of all the signals at each point in time. In someembodiments, the average signal is the 99th percentile of all thesignals at each point in time. In some embodiments, the result of thisaveraging is a single signal with uniform sampling over time. In step290, the exemplary inventive computing device is programmed/configuredto normalize the signal calculated in step 280. In some embodiments, thesignal is normalized by dividing by a constant factor. In someembodiments, the constant factor is between 2 volts and 1000 volts. Insome embodiments, the constant factor is 50 volts. FIG. 12A shows anexemplary normalized electrical uterine signal following the performanceof steps 280 and 290. FIG. 12B shows a tocograph signal generated overthe same time period, with contractions self-reported by the motherindicated by vertical lines. Referring to FIGS. 12A and 12B, it can beseen that the peaks in the exemplary normalized electrical uterinesignal in FIG. 12A coincide with the self-reported contractions shown inFIG. 12B. Accordingly, in some embodiments, a normalized electricaluterine monitoring (“EUM”) signal produced through the performance ofthe exemplary method 200 (e.g., the signal shown in 12A) is suitable foruse to identify contractions. In some embodiments, a contraction isidentified by identifying a peak in the EUM signal.

In some embodiments, the present invention is directed to a specificallyprogrammed computer system, including at least the following components:a non-transient memory, electronically storing computer-executableprogram code; and at least one computer processor that, when executingthe program code, becomes a specifically programmed computing processorthat is configured to perform at least the following operations:receiving a plurality of bio-potential signals collected at a pluralityof locations on the abdomen of a pregnant mother; detecting R-wave peaksin the bio-potential signals; extracting maternal electrocardiogram(“ECG”) signals from the bio-potential signals; determining R-waveamplitudes in the maternal ECG signals; creating an R-wave amplitudesignal for each of the maternal ECG signals; calculating an average ofall the R-wave amplitude signals; and normalizing the average to producean electrical uterine monitoring (“EUM”) signal. In some embodiments,the operations also include identifying at least one uterine contractionbased on a corresponding at least one peak in the EUM signal.

FIG. 13 shows a flowchart of a first exemplary inventive method 1300. Insome embodiments, an exemplary inventive computing device,programmed/configured in accordance with the method 1300, is operativeto receive, as input, raw bio-potential data measured by a plurality ofelectrodes positioned on the skin of a pregnant human subject, andanalyze such input to produce a tocograph-like signal. In someembodiments, the quantity of electrodes is between 2 and 10. In someembodiments, the quantity of electrodes is between 2 and 20. In someembodiments, the quantity of electrodes is between 2 and 30. In someembodiments, the quantity of electrodes is between 2 and 40. In someembodiments, the quantity of electrodes is between 4 and 10. In someembodiments, the quantity of electrodes is between 4 and 20. In someembodiments, the quantity of electrodes is between 4 and 30. In someembodiments, the quantity of electrodes is between 4 and 40. In someembodiments, the quantity of electrodes is between 6 and 10. In someembodiments, the quantity of electrodes is between 6 and 20. In someembodiments, the quantity of electrodes is between 6 and 30. In someembodiments, the quantity of electrodes is between 6 and 40. In someembodiments, the quantity of electrodes is between 8 and 10. In someembodiments, the quantity of electrodes is between 8 and 20. In someembodiments, the quantity of electrodes is between 8 and 30. In someembodiments, the quantity of electrodes is between 8 and 40. In someembodiments, the quantity of electrodes is 8. In some embodiments, theexemplary inventive computing device, programmed/configured inaccordance with the method 1300, is operative to receive, as input,maternal ECG signals that have already been extracted from rawbio-potential data (e.g., by separation from fetal ECG signals that formpart of the same raw bio-potential data). In some embodiments, theexemplary inventive computing device is programmed/configured inaccordance with the method 1300 via instructions stored in anon-transitory computer-readable medium. In some embodiments, theexemplary inventive computing device includes at least one computerprocessor, which, when executing the instructions, becomes aspecifically-programmed computer processor programmed/configured inaccordance with the method 1300.

In some embodiments, the exemplary inventive computing device isprogrammed/configured to continuously perform one or more steps of themethod 1300 along a moving time window. In some embodiments, the movingtime window has a predefined length. In some embodiments, the predefinedlength is sixty seconds. In some embodiments, the exemplary inventivecomputing device is programmed/configured to continuously perform one ormore steps of the method 1300 along a moving time window having a lengththat is between one second and one hour. In some embodiments, the lengthof the moving time window is between thirty seconds and 30 minutes. Insome embodiments, the length of the moving time window is between 30seconds and 10 minutes. In some embodiments, the length of the movingtime window is between 30 seconds and 5 minutes. In some embodiments,the length of the moving time window is about 60 seconds. In someembodiments, the length of the moving time window is 60 seconds.

In step 1305, the exemplary inventive computing device isprogrammed/configured to receive raw bio-potential data as input.Exemplary raw bio-potential data is shown in FIGS. 18A, 18C, 18E, and18G. In some embodiments, the raw bio-potential data is recorded throughthe use of at least two electrodes positioned in proximity to a pregnantsubject's skin. In some embodiments, at least one of the electrodes is asignal electrode. In some embodiments at least one of the electrodes isa reference electrode. In some embodiments, the reference electrode islocated at a point away from the uterus of the subject. In someembodiments, a bio-potential signal is recorded at each of severalpoints around the pregnant subject's abdomen. In some embodiments, abio-potential signal is recorded at each of eight points around thepregnant subject's abdomen. In some embodiments, the bio-potential datais recorded at 1,000 samples per second. In some embodiments, thebio-potential data is up-sampled to 1,000 samples per second. In someembodiments, the bio-potential data is recorded at a sampling rate ofbetween 100 and 10,000 samples per second. In some embodiments, thebio-potential data is up-sampled to a sampling rate of between 100 and10,000 samples per second. In some embodiments, the steps of the method1300 between receipt of raw data and channel selection (i.e., step 1310through step 1335) are performed on each of a plurality of signalchannels, wherein each signal channel is generated by the exemplaryinventive computing device as the difference between the bio-potentialsignals recorded by a specific pair of the electrodes. In someembodiments, in which the method 1300 is performed through the use ofdata recorded at electrodes located as shown in FIGS. 4A and 4B,channels are identified as follows:

Channel 1: A1-A4

Channel 2: A2-A3

Channel 3: A2-A4

Channel 4: A4-A3

Channel 5: B1-B3

Channel 6: B1-B2

Channel 7: B3-B2

Channel 8: A1-A3

In step 1310, the exemplary inventive computing device isprogrammed/configured to pre-process the signal channels determinedbased on the raw bio-potential data to produce a plurality ofpre-processed signal channels. In some embodiments, the pre-processingincludes one or more filters. In some embodiments, the pre-processingincludes more than one filter. In some embodiments, the pre-processingincludes a DC removal filter, a powerline filter, and a high passfilter. In some embodiments, a DC removal filter removes the raw data'smean at the current processing interval. In some embodiments, thepowerline filter includes a 10^(th)-order band-stop infinite impulseresponse (“IIR”) filter that is configured to minimize any noise at apreconfigured frequency in the data. In some embodiments, thepreconfigured frequency is 50 Hz and the powerline filter includescutoff frequencies of 49.5 Hz and 50.5 Hz. In some embodiments, thepreconfigured frequency is 60 Hz and the powerline filter includescutoff frequencies of 59.5 Hz and 60.5 Hz. In some embodiments, highpass filtering is performed by subtracting a wandering baseline from thesignal, where the baseline is calculated through a moving average windowhaving a predetermined length. In some embodiments, the predeterminedlength is between 50 milliseconds and 350 milliseconds. In someembodiments, the predetermined length is between 100 milliseconds and300 milliseconds. In some embodiments, the predetermined length isbetween 150 milliseconds and 250 milliseconds. In some embodiments, thepredetermined length is between 175 milliseconds and 225 milliseconds.In some embodiments, the predetermined length is about 200 milliseconds.In some embodiments, the predetermined length is 201 milliseconds (i.e.,50 samples at a sampling rate of 250 samples per second) long. In someembodiments, the baseline includes data from frequencies lower than 5Hz,and thus the signal is high pass filtered at about 5Hz. Pre-processeddata generated based on the raw bio-potential data shown in FIGS. 18A,18C, 18E, and 18G is shown in FIGS. 18B, 18D, 18F, and 18H,respectively.

Continuing to refer to step 1310, in some embodiments, followingapplication of the filters described above, each data channel is checkedfor contact issues. In some embodiments, contact issues are identifiedin each data channel based on at least one of (a) RMS of the datachannel, (b) signal-noise ratio (“SNR”) of the data channel, and (c)time changes in peaks relative energy of the data channel. In someembodiments, a data channel is identified as corrupted if it has an RMSvalue greater than a threshold RMS value. In some embodiments, thethreshold RMS value is two local voltage units (e.g., a value of about16.5 millivolts). In some embodiments, the threshold RMS value isbetween one local voltage unit and three local voltage units. Anexemplary data channel identified as corrupted on this basis is shown inFIGS. 18A and 18B. In some embodiments, a data channel is identified ascorrupted if it has a SNR value less than a threshold SNR value. In someembodiments, the threshold SNR value is 50 dB. In some embodiments, thethreshold SNR value is between 40 dB and 60 dB. In some embodiments, thethreshold SNR value is between 30 dB and 70 dB. An exemplary datachannel identified as corrupted on this basis is shown in FIGS. 18C and18D. In some embodiments, a data channel is identified as corrupted ifit has a change in relative R-wave peak energy from one interval toanother that is greater than a threshold amount of change. In someembodiments, the threshold amount of change is 250%. In someembodiments, the threshold amount of change is between 200% and 300%. Insome embodiments, the threshold amount of change is between 150% and350%. An exemplary data channel identified as corrupted on this basis isshown in FIGS. 18E and 18F. An exemplary data channel not identified ascorrupted for any of the above reasons is shown in FIGS. 18G and 18H.

In step 1315, the exemplary inventive computing device isprogrammed/configured to extract R-wave peaks from the pre-processedsignal channels to produce R-wave peak data sets. In some embodiments,step 1315 uses as input known maternal ECG peaks. In some embodiments,step 1315 uses as input maternal ECG peaks identified in accordance withthe techniques described in U.S. Pat. No. 9,392,952. In someembodiments, step 1315 includes using the preprocessed data (e.g., asproduced by step 1310) and the known maternal ECG peaks to refine thematernal ECG peak positions. In some embodiments, peak positionrefinement includes a search for the maximal absolute value in a windowof samples before and after the known maternal ECG peaks to ensure theR-wave peak is positioned at the maximum point of the R waves for eachone of the filtered signals. In some embodiments, the window includesplus or minus a predetermined length of time. In some embodiments, thepredetermined length is between 50 milliseconds and 350 milliseconds. Insome embodiments, the predetermined length is between 100 millisecondsand 300 milliseconds. In some embodiments, the predetermined length isbetween 150 milliseconds and 250 milliseconds. In some embodiments, thepredetermined length is between 175 milliseconds and 225 milliseconds.In some embodiments, the predetermined length is about 200 milliseconds.In some embodiments, the window includes plus or minus a number ofsamples that is in a range between one sample and 100 samples.Illustration of the known maternal ECG peaks and the extracted R-wavepeaks in an exemplary R-wave peak data set are shown in FIGS. 19A and19B, respectively.

In step 1320, the exemplary inventive computing device isprogrammed/configured to remove electromyography (“EMG”) artifacts fromthe data, which includes the preprocessed data produced by step 1310 andthe R-wave peaks extracted in step 1315. FIG. 20A shows exemplarypreprocessed data used as input to step 1320. In some embodiments,removal of EMG artifacts is performed in order to correct for peaks withhigh amplitude where there is an increase in high frequency energy,which usually, but not always, originates from maternal EMG activity.Other sources of such energy are high powerline noise and high fetalactivity. In some embodiments, removal of EMG artifacts includes findingcorrupted peaks and replacing them with a median value. In someembodiments, finding corrupted peaks includes calculating inter-peak RMSvalues based on the following formula:

The first step of correcting this artefact is finding the corruptedpeaks. Doing so requires calculating the inter-peaks RMS values thus:

inter peaks RMS (iPeak)=RMS(peaks signal(peak location(iPeak)+1:peaklocation(iPeak+1)−1))

In the above formula, peaks signal is the signal with R-peaks heights(i.e., the amplitude of the R-wave peaks) and peak location is thesignal with R-peaks time-indices found per each channel (i.e., the timeindex for each of the R-wave peaks). In some embodiments, there are twopeaks signal values and two peak location values, one for R-wave peaksfound using the filtered data and one found using the opposite signal(i.e., a signal obtained by multiplying the original signal data by -1to yield a sign-inverted signal).

In some embodiments, finding corrupted peaks also includes findingoutlier peaks in a maternal physical activity (“MPA”) data set. In someembodiments, such signals (referred to as “envelope signals”hereinafter) are extracted as follows:

In some embodiments, physical activity data is collected using motionsensors. In some embodiments, the motion sensors include a tri-axialaccelerometer and a tri-axial gyroscope. In some embodiments, the motionsensors are sampled 50 times per second (50 sps). In some embodiments,the sensors are located on a same sensing device (e.g., a wearabledevice) that contains electrodes used to collect bio-potential data forthe performance of the method 1300 as a whole (e.g., the device 300shown in FIG. 3).

In some embodiments, raw motion data is converted. In some embodiments,raw motion data is converted to g units in the case of accelerometer rawdata and degrees per second in the case of gyroscope raw data. In someembodiments, the converted data is examined to distinguish between validand invalid signals by determining whether the raw signals are saturated(e.g., that they have a constant maximal possible value). In someembodiments, signal envelope is extracted as follows. First, in someembodiments, the data is checked for position change. Since positionchange is characterized by an increase in accelerometer baseline, insome embodiments a baseline filter is applied whenever a position changeoccurs. In some embodiments, filtration is performed by employing ahigh-pass finite impulse response (“FIR”) filter. In some embodiments,the high-pass filter has a filter order of 400 and a frequency of 1Hertz. In some embodiments, to eliminate any non-physiological movement,a low-pass FIR filter is also applied. In some embodiments, the low-passfilter has a filter order of 400 and a frequency of 12 Hertz. (order400, fc=12 Hz [1]) is applied as well. In some embodiments, followingfiltering, the magnitude of the accelerometer vector is calculated inaccordance with the below formula:

${{AccMagnitudeVector}({iSample})} = \sqrt{\begin{matrix}{\begin{pmatrix}{{{AccD}at{a\left( {x,{iSample}} \right)}^{2}} +} \\{{AccDat}{a\left( {y,{iSample}} \right)}^{2}}\end{pmatrix} +} \\{{AccData}\left( {z,{iSamp{le}}} \right)}^{2}\end{matrix}}$

In this formula, AccMagnitudeVector(iSample) represents the square rootof the sum of the squares of the three accelerometer axes (e.g., x, y,and z) for sample number iSample. In some embodiments, the magnitudevector of the gyroscope data is calculated in accordance with thefollowing formula:

${{GyroMagnitudeVector}({iSample})} = \sqrt{\begin{pmatrix}\begin{matrix}{{{Gyro}Dat{a\left( {x,{iSample}} \right)}^{2}} +} \\{{{GyroDat}{a\left( {y,{iSample}} \right)}^{2}} +}\end{matrix} \\{{GyroData}\left( {z,{iSample}} \right)^{2}}\end{pmatrix}}$

In this formula, GyroMagnitudeVector(iSample) represents the square rootof the sum of the squares of the three gyroscope axes (e.g., x, y, andz) for sample number iSample. In some embodiments, following calculationof both the accelerometer magnitude vector and the gyroscope magnitudevector, envelopes of the gyroscope's magnitude vector and theaccelerometer's magnitude vector are extracted by applying an RMS windowto the gyroscope's magnitude vector and the accelerometer's magnitudevector, respectively. In some embodiments, the RMS window is 50 samplesin length. In some embodiments, following extraction of the envelopes ofthe gyroscope's magnitude vector and the accelerometer's magnitudevector, the two envelopes are averaged (e.g., mean average, median,etc.) to produce an MPA motion envelope.

In some embodiments, peaks in the MPA motion envelope are defined inaccordance with the following steps:

motion envelope peaks=find(motion envelope>P₉₅% (motion envelope))

motion envelope peaks onset=motion envelope peaks−2·peak width

motion envelope peaks offset=motion envelope peaks+2·peak width

In the above, peak width is defined as the distance between the peak andthe first point where the envelope reaches 50% of the peak value andP₉₅%(x) is the 95th percentile of x. FIG. 20B shows the data signal ofFIG. 20A along with a corresponding motion envelope calculated inaccordance with the above and an inter-peaks absolute sum (i.e., the sumof the absolute values of all samples falling in between adjacentpeaks).

In some embodiments, peaks are determined to be corrupt if they are:

1) Peaks with inter-peaks RMS higher than 20 local voltage units

2) In case the signal examination stage concluded there is a contactissue in the current processing interval, peaks with inter-peaks RMShigher than 8 local voltage units

3) In case the signal examination stage concluded there is a contactissue in the current processing interval, but more than 50% of pointshave inter-peaks RMS higher than 8 local voltage units, use the 20 localvoltage units threshold

4) Peaks positioned around the motion envelope onset and offset aresuspected to be corrupt. These points' inter-peaks RMS should exceed 6to conclude they are corrupt.

In some embodiments, if a peak is detected to be a corrupted peak asdescribed above, the amplitude of the peak is replaced with a medianvalue, where local median value around the corrupted peak is calculatedas follows:

local median=median(peaks signal(corrupt peak−10: corrupt peak+10))

In some embodiments, the corrupted data points themselves are excludedfrom the above calculation and replaced with a statistical value (e.g.,a global median, a local median, a mean, etc.). In some embodiments, ifthere are 7 or less values to use after exclusion, use the global medianas the local one, where the global median is calculated using standardtechniques:

local median=global median=median(signal)

In some embodiments, if the absolute difference between the local medianand global median exceeds 0.1, the local median is used in place of theamplitude of the corrupted data point, and otherwise the global medianis used as a replacement for the corrupted peaks' amplitude. FIG. 20Cshows the exemplary data set of FIGS. 20A and 20B with corrupted peaksreplaced as discussed above.

Continuing to refer to FIG. 13, in step 1325, the exemplary inventivecomputing device is programmed/configured to remove baseline artifactsfrom the signal formed by the R-wave peaks. In some embodiments, suchartifacts are caused by sudden baseline or RMS changes. In someembodiments, such changes are often caused by maternal position changes.FIG. 21A shows an exemplary data signal that includes a baselineartifact.

In some embodiments, such artifacts are found using the Grubbs test foroutliers, which is a statistical test performed based on absolutedeviation from sample mean. In some embodiments, to correct suchartifacts, a point of change should be found at first. In someembodiments, a point of change is a point (e.g., data point) where achange in signal RMS or mean begins; such a point should satisfy thefollowing criteria:

length(peaks signal)−change point>50   1)

prctile(peaks signal(change point:end),10)>0.01   2)

$\begin{matrix}{{1.5 < \frac{P_{10\%}\left( {{peaks}\mspace{14mu} {signal}\mspace{14mu} \left( {{1\text{:}\mspace{11mu} {change}\mspace{14mu} {point}} - 1} \right)} \right)}{P_{10\%}\left( {{peaks}\mspace{14mu} {signal}\mspace{14mu} \left( {{change}\mspace{14mu} {point}\text{:}\mspace{11mu} {end}} \right)} \right)} < 2}{\left( {{where}\mspace{14mu} {P_{10\%}(x)}\mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} 10^{th}\mspace{14mu} {percentile}\mspace{14mu} {of}\mspace{14mu} x} \right)\mspace{14mu} {OR}}} & \left. {3a} \right) \\{0 < \frac{P_{10\%}\left( {{peaks}\mspace{14mu} {{signal}\left( {{1\text{:}\mspace{11mu} {change}\mspace{14mu} {point}} - 1} \right)}} \right)}{P_{10\%}\left( {{peaks}\mspace{14mu} {signal}\mspace{14mu} \left( {{change}\mspace{14mu} {point}\text{:}\mspace{11mu} {end}} \right)} \right)} < 0.8} & \left. {3b} \right)\end{matrix}$

In some embodiments, should the change point satisfy the above-mentionedcriteria, the peak signal up to this point is changed based on astatistical value as defined below:

${{peaks}\mspace{14mu} {signal}\mspace{14mu} \left( {{1\text{:}\mspace{11mu} {change}\mspace{14mu} {point}} - 1} \right)} = {{\frac{P_{10\%}\begin{pmatrix}{{peaks}\mspace{14mu} {signal}} \\\left( {{change}\mspace{14mu} {point}\text{:}\mspace{11mu} {end}} \right)\end{pmatrix}}{P_{10\%}\begin{pmatrix}{{peaks}\mspace{14mu} {signal}} \\\left( {{1\text{:}\mspace{11mu} {change}\mspace{14mu} {point}} - 1} \right)\end{pmatrix}} \cdot {peaks}}\mspace{14mu} {signal}\mspace{14mu} \left( {{1\text{:}\mspace{11mu} {change}\mspace{14mu} {point}} - 1} \right)}$

FIG. 21B shows the exemplary data signal of FIG. 21A after baselineartifact removal has been performed in accordance with step 1330.

Continuing to refer to FIG. 13, in step 1330, the exemplary inventivecomputing device is programmed/configured to remove outliers from theR-wave peaks signal using an iterative process according to a Grubbstest for outliers. FIG. 22A shows an exemplary R-wave peaks signal thatincludes an outlier data point, as indicated by a diamond. In someembodiments, the iterative process of step 1330 stops when either of thefollowing two conditions occurs:

$\begin{matrix}{{{Outliers}\mspace{14mu} \frac{P_{95\%}}{P_{50\%}}\mspace{14mu} {ratio}} > 1.5} & \left. 1 \right) \\{{{Iteration}\mspace{14mu} {number}} > 4} & \left. 2 \right)\end{matrix}$

In some embodiments, this process finds outlier points at each iterationand trims the height of such outlier points to the median value of thelocal area around the outlier peak. In some embodiments, the local areais defined as a time window of a predetermined number of samples beforeand after the outlier peak. In some embodiments, the predeterminednumber of samples is between zero and twenty. In some embodiments, thepredetermined number of samples is ten. FIG. 22B shows the exemplarydata signal of FIG. 22A following the performance of step 1330. It maybe seen that the outlier data point shown in FIG. 22A is no longerpresent in FIG. 22B. In some embodiments, following signal extraction,more outliers are revealed and removed, as will be described in furtherdetail hereinafter.

Continuing to refer to FIG. 13, in step 1335, the exemplary inventivecomputing device is programmed/configured to interpolate and extractR-wave signal data from each of the R-wave peaks signal data sets toproduce R-wave signal channels In some embodiments, the peaks signalthat is output by step 1330 is interpolated in time to provide a 4samples-per-second signal. FIG. 23A shows an exemplary peaks signaloutput by step 1330. In some embodiments, interpolation is done usingcubic spline interpolation. In some embodiments in cases of large gapsin the interpolated data, erroneous high values are present, and insteadthe interpolation method is shape-preserving piecewise cubicinterpolation. In some embodiments, the shape-preserving piecewise cubicinterpolation is piecewise cubic hermite interpolating polynomial(“PCHIP”) interpolation. In some embodiments, following interpolation,the process of extracting an R-wave signal includes identifying furtheroutliers in the interpolated signal. In some embodiments, furtheroutliers are identified in this step as one of the following:

1) Signal peaks (i.e., peaks in the R-wave signal after interpolation,not peaks in the raw bio-potential signal) with height larger than 1local voltage unit and its surroundings

2) Points lying between two consecutive R-peaks that are more than 10seconds apart

3) Minutes where a severe contact issue was found during the dataexamination stage (e.g., during steps 1320, 1335, and 1330)

In some embodiments, points that are identified as outliers based onmeeting any of the three criteria mentioned above are discarded and arereplaced by a statistical value (e.g., either a local median or a globalmedian) in accordance with the process described above with reference tostep 1320.

Continuing to describe step 1335, in some embodiments, following furtheroutlier detection, signal statistics (e.g., median value, minimum value,and standard deviation) are calculated, and a signal (e.g., a one-minutesignal time window for a given channel) is identified as a corruptedsignal if any of the following are true:

1) After elimination of outlier points, the signal still has peaks withamplitude larger than one local voltage unit and standard deviationgreater than 0.1

2) The signal has a median value greater than 0.65 and a minimum lessthan 0.6

3) More than 15% of the points comprising the signal have been deletedas outliers

Continuing to describe step 1335, following identification of corruptsignals, a sliding RMS window is applied to the signal. In someembodiments, the RMS window has a size that is in the range of between25 and 200 samples. In some embodiments, the RMS window has a size of100 samples. In some embodiments, following application of an RMSwindow, a first order polynomial function is fitted to the signal andthen subtracted from the signal, thereby producing a clean version ofthe interpolated signal, which may be used for the subsequent steps.FIG. 23B shows an exemplary R-wave signal following interpolation ofstep 1330.

Continuing to refer to FIG. 13, in step 1340, the exemplary inventivecomputing device is programmed/configured to perform channel selection,whereby a subset of the exemplary R-wave signal channels is selected foruse in generating an electrical uterine monitoring signal. In someembodiments, at the start of channel selection all channels areconsidered to be eligible candidates, and channels are evaluated forpossible exclusion in accordance with the following:

1) Exclude any channels with contact issues in more than 10% of theprocessing intervals up to the present time

2) If more than 50% of channels are excluded on the basis of the above,exclude instead all channels with contact issues in more than 15% of theprocessing intervals

If the above results in all channels being excluded, then, instead, anychannels that satisfy both of the following criteria are retained, withthe remaining channels excluded:

1) Standard deviation of the signal is between 0 and 0.1

2) Range of the signal is less than 0.2

If the above still results in all channels being excluded, then only thefirst above condition relating to standard deviation is used, and thesecond above condition relating to range is disregarded. FIG. 24A showsan exemplary data set including six data channels, with two datachannels having been excluded.

In some embodiments, following removal of some channels as describedabove, the remaining channels are grouped into couples. In someembodiments, in which channels are defined as described above, a channelcouple is any pair of the eight channels discussed above. In someembodiments, only couples that are independent of one another (i.e.,couples that have no electrode in common) are considered. In someembodiments, possible couples are as follows:

2) 1. channels 1 and 2 (A1-A4 and A2-A3)

2) 2. channels 1 and 5 (A1-A4 and B1-B3)

2) 3. channels 1 and 6 (A1-A4 and B1-B2)

2) 4. channels 1 and 7 (A1-A4 and B3-B2)

2) 5. channels 2 and 5 (A2-A3 and B1-B3)

2) 6. channels 2 and 6 (A2-A3 and B1-B2)

2) 7. channels 2 and 7 (A2-A3 and B3-B2)

2) 8. channels 3 and 5 (A2-A4 and B1-B3)

2) 9. channels 3 and 6 (A2-A4 and B1-B2)

2) 10. channels 3 and 7 (A2-A4 and B3-B2)

2) 11. channels 3 and 8 (A2-A4 and A1-A3)

2) 12. channels 4 and 5 (A4-A3 and B1-B3)

2) 13. channels 4 and 6 (A4-A3 and B1-B2)

2) 14. channels 4 and 7 (A4-A3 and B3-B2)

2) 15. channels 5 and 8 (B1-B3 and A1-A3)

2) 16. channels 6 and 8 (B1-B2 and A1-A3)

2) 17. channels 7 and 8 (B3-B2 and A1-A3)

As may be seen, for each of the channel pairs listed above, the twochannels forming the pair do not share a common electrode. In someembodiments, the Kendall rank correlation of each couple of channels iscalculated using only valid points within the channels. In someembodiments, Kendall correlation counts the matching rank signs of eachpair of signals to test their statistical dependency.

In some embodiments, channels are then selected by the followingselection criteria. First, if the maximum Kendall correlation value isgreater than or equal to 0.7, the selected channels are any independentchannels having Kendall correlation values greater than or equal to 0.7.However, if all selected channels were previously identified ascorrupted, then the output signal is identified as a corrupted signal.Additionally, if any of the selected channels was previously identifiedas corrupted, or if any of the selected channels has a range greaterthan 0.3, then any such channels are excluded from the selectedchannels.

Second, if none of the channels were selected under the first criterionnoted above, then if the maximum Kendall correlation value is greaterthan or equal to 0.5 but less than 0.7, the selected channels are anyindependent channels having Kendall correlation values in this range.However, if all selected channels were previously identified ascorrupted, then the output signal is identified as a corrupted signal.Additionally, if any of the selected channels was previously identifiedas corrupted, or if any of the selected channels has a range greaterthan 0.3, then any such channels are excluded from the selectedchannels.

Third, if none of the channels were selected under the first or secondcriteria noted above, then if the maximum Kendall correlation value isgreater than zero but less than 0.5, then all channels having Kendallcorrelation values greater than zero are identified as selectedchannels. However, if the maximal correlation value is less than 0.3,then the output signal is marked as corrupted and all channels withrange greater than 0.3 are excluded.

Fourth, if none of the channels were selected under the first threecriteria noted above, then all channels with range greater than 0.3 andall channels with more than 15% deleted points are excluded, theremaining channels are selected, and this output signal is identified asone that should be less sharpened, as will be discussed hereinafter withreference to step 1355.

Fifth, if none of the channels were selected under any of the fourcriteria noted above, then all channels are selected other than thosethat have severe contact issues. However, if the number of contactissues in the selected channels exceeds fifteen, then the output signalis flagged as corrupted. FIG. 24B shows an exemplary data set followingchannel selection of step 1340.

In some embodiments, rather than selecting channels in pairs based oncorrelation values of the pairs, channels are selected individually.

Continuing to refer to FIG. 13, in step 1345, the exemplary inventivecomputing device is programmed/configured to calculate a uterineactivity signal (which may be referred to as an “electrical uterinemonitoring” or “EUM” signal) based on the selected R-wave signalchannels selected in step 1340. In some embodiments, for each sample(e.g., set of data points at a given sampling time during the foursamples per second sampling interval discussed above, for all selectedchannels), the 80^(th) percentile of the selected channels' signals iscalculated in accordance with the following:

combined signal(iSample)=P₈₀% (interpolated peaks signal(selectedchannels, iSample))

FIG. 25A shows an 80^(th) percentile signal calculated based on theselected data channels shown in FIG. 24B. In some embodiments, awandering baseline is then removed from the combined 80^(th) percentilesignal as determined above to produce an EUM signal. In someembodiments, a moving average window is considered to find the baseline.In some embodiments, the moving average window subtracts the mean valueduring the window from the EUM signal. In some embodiments, the lengthof the window is between zero minutes and twenty minutes. In someembodiments, the length of the window is ten minutes. FIG. 25B shows theexemplary signal of FIG. 25A following removal of the baseline.

In step 1350, the exemplary inventive computing system isprogrammed/configured to normalize the EUM signal calculated in step1345. In some embodiments, normalization consists of multiplying the EUMsignal from step 1345 by a constant. In some embodiments, the constantis between 200 and 500. In some embodiments, the constant is between 250and 450. In some embodiments, the constant is between 300 and 400. Insome embodiments, the constant is between 325 and 375. In someembodiments, the constant is about 350. In some embodiments, theconstant is 350. In some embodiments, the constant is 1, i.e., theoriginal values of the extracted 80^(th) percentile signal aremaintained. FIG. 26 shows an exemplary data signal followingnormalization of the data signal of FIG. 25B in accordance with step1350.

In step 1355, the exemplary inventive computing system isprogrammed/configured to sharpen the normalized EUM signal produced bystep 1350, thereby producing a sharpened EUM signal. In someembodiments, sharpening is performed only on signals that were notflagged as corrupted in the preceding steps; if all relevant signals areflagged as corrupted, then the sharpening step is not performed. In someembodiments, the objective of the sharpening step is to enhance allareas with suspected contractions. In some embodiments, sharpeningproceeds as follows. First, if there are any peaks in the EUM signalexceeding the values of 200 local voltage units, the signal is marked ascorrupted. Second, it is determined whether the signal was previouslymarked as corrupted. Third, the signal baseline is removed. In someembodiments, for baseline removal, if the signal duration exceeds tenminutes then a ten-minute long moving average window is used to estimatethe baseline, and otherwise the signal's 10^(th) percentile is used toestimate the baseline; in either case, the baseline is then subtractedfrom the EUM signal. Fourth, the signal baseline is defined as 30visualization voltage units. In some embodiments, a signal baselinedefined in this manner following the normalization step provides for anEUM signal that is within a 0-100 range in a manner similar to thesignal provided by a cardiotocograph.

Fifth, peaks are identified in accordance with one of the following:

If the signal was identified as one that needs less sharpening duringstep 1340, then peaks are defined as having a minimum height of 35visualization voltage units and a minimum width of 300 samples.

If the signal was not so identified, peaks are identified as having aminimum height of 35 visualization units and a minimum width of 220samples.

In either case, the prominence of each peak is calculated in accordancewith the below formula:

peaks prominence=peaks height−P_(100%)(EUM signal)

Following the calculation of the prominence for all peaks in the sample,each peak is eliminated if either of the below is true for that peak:

The peak has a prominence less than 12 and a height less than 40visualization voltage units

The peak has a prominence less than 65% of the maximum prominence of allof the peaks in the sample.

In some embodiments, additional peaks are identified by identifying anyfurther peaks (e.g., local maxima) with a minimum height of 15visualization voltage units and a minimum width of 200 samples, and theneliminating all peaks with a prominence higher than 20 visualizationvoltage units.

Following the above, sharpening is performed only if all of thefollowing are true: (a) the signal is not corrupt (with “corrupt”signals being identified as described above); (b) there are no deletedpoints in the signal; and (c) at least one peak was identified in thepreceding portions of this step. If sharpening is to be performed, then,prior to sharpening, each peak is eliminated if any of the belowconditions are true for that peak:

The peak has a prominence of less than 10 visualization voltage units

The peak has a prominence of greater than 35 visualization voltage units

The peak has a width of more than 800 samples (i.e., 200 seconds at 4samples per second)

Following elimination of any peaks that meet one of the conditions notedabove, the following values are calculated for each remaining peak:

μ = mean  (peak  start, peak  end)$\sigma = \frac{\left( {{{peak}\mspace{14mu} {end}} - {{peak}\mspace{14mu} {start}}} \right)}{2\sqrt{2\pi}}$t = peak  start:  peak  end

Once these values have been calculated, a mask of zero values outsidethe peak areas and Gaussian functions inside the peak areas is createdin accordance with the following formula:

${{mask}\left( t_{ithpeak} \right)} = {\exp \left( {- \frac{\left( {t_{ithpeak} - \mu_{ithpeak}} \right)^{2}}{2\sigma_{ithpeak^{2}}}} \right)}$

The mask is then smoothed with a moving average window having apredefined length. In some embodiments, the predefined length is between10 seconds and 50 seconds. In some embodiments, the predefined length isbetween 20 seconds and 40 seconds. In some embodiments, the predefinedlength is between 25 seconds and 35 seconds. In some embodiments, thepredefined length is about 30 seconds. In some embodiments, thepredefined length is 30 seconds. An exemplary EUM signal is shown inFIG. 27A and an exemplary mask created in the above manner for theexemplary EUM signal of FIG. 27A is shown in FIG. 27B. The mask is thenadded to the existing EUM signal to produce a sharpened EUM signal. Insome embodiments, the addition is performed using simple mathematicaladdition. An exemplary sharpened EUM signal produced by adding theexemplary mask of FIG. 27B to the exemplary EUM signal of FIG. 27A isshown in FIG. 27C.

Referring back to FIG. 13, in step 1360, post-processing is performed toproduce a post-processed EUM signal. In some embodiments,post-processing includes baseline removal. In some embodiments, baselineremoval includes removing a signal baseline as described above withreference to step 1355. In some embodiments, for baseline removal, ifthe signal duration exceeds ten minutes then a ten-minute long movingaverage window is used to estimate the baseline, and otherwise thesignal's 10th percentile is used to estimate the baseline; in eithercase, the baseline is then subtracted from the EUM signal, and thesignal baseline is defined as 30 visualization voltage units. Last, alldeleted values are set to a value of −1 visualization voltage unit andall values above 100 visualization voltage units are set to a value of100 visualization voltage units. FIG. 28 shows an exemplarypost-processed signal generated by applying the post-processing of step1360 to the exemplary sharpened signal of FIG. 27B.

Following step 1360, the method 1300 is complete. As noted above, FIG.28 shows an exemplary EUM signal calculated in accordance with themethod 1300. FIG. 29 shows a representative tocograph signal obtained inaccordance with known techniques for the same subject and during thesame time period as collection of the bio-potential data based on whichthe EUM signal of FIG. 28 was calculated. It may be seen that FIGS. 28and 29 are substantially similar to one another and include the samepeaks, which may be understood to represent contractions. Accordingly,it may be seen that the result of the method 1300 is an EUM signal thatis usable as a tocograph-like signal to monitor maternal uterineactivity, but which can be calculated based on bio-potential signalsthat are recorded non-invasively.

Reference is now made to the following examples, which together with theabove descriptions illustrate some embodiments of the invention in anon-limiting fashion.

EXAMPLES

FIGS. 14A-17B show further examples of comparisons between tocographdata and the output of the exemplary method 200. In each of FIGS. 14A,15A, 16A, and 17A, a tocograph signal against time is shown, withcontractions self-reported by the mother being monitored by thetocograph indicated with vertical lines. In each of FIGS. 17B, 15B, 16B,and 17B, the filtered R-wave signal from each of a plurality of channelsis shown in a different color (e.g., similar to the plot shown in FIG.11B), with the calculated normalized average signal shown in a heavyblack line (e.g., similar to the plot shown in FIG. 12A). Each of FIGS.14B, 15B, 16B and 17B is shown adjacent to the corresponding one ofFIGS. 14A, 15A, 16A, and 17A for comparison (e.g., FIGS. 14A and 14Bshow different data recorded for the same mother over the same timeinterval, and so on for FIGS. 15A through 17B). As discussed above withreference to FIGS. 12A and 12B, it can be seen that the peaks in theexemplary normalized uterine signal correspond to the self-reportedcontractions.

A study was conducted to evaluate the effectiveness of the exemplaryembodiments. The study involved a comparison of EUM and TOCO recordingsin pregnant women aged 18-50 years with a BMI of <45 kg/m², carrying asingleton fetus at gestational age >32+0 weeks, without fetal anomalies.EUM was calculated as described above over data samples measured for aminimum of 30 minutes. Analysis of the maternal cardiac R-waveamplitude-based uterine activity index referred to herein as EUM showedpromising results as an innovative and reliable method for monitoringmaternal uterine activity. The EUM data correlated highly with TOCOdata. Accordingly, EUM monitoring may provide data that is similarlyuseful to TOCO data, while overcoming the shortcomings of traditionaltocodynamometry, such as discomfort.

FIGS. 18A through 27B show exemplary data existing at various stagesduring performance of the exemplary method 1300. In particular, FIGS.27A and 27B show a comparison of the output signal generated by theexemplary method 1300 to a tocograph signal recorded during the sametime interval.

FIGS. 18A-18H show exemplary raw data that is received as input for theexemplary method 300 (e.g., as received in step 1305) and exemplaryfiltered raw data produced during the exemplary method 1300 (e.g., asproduced by step 1310). In particular, FIGS. 18A, 18C, 18E, and 18G showexemplary raw data, while FIGS. 18B, 18D, 18F, and 18H respectively,show exemplary filtered data. It will be apparent to those of skill inthe art that FIGS. 18A-18H represent raw and filtered bio-potential datafor a single channel and that, in a practical implementation of themethod 1300 as described above, data sets comparable to those shown inFIGS. 18A-18H will be present for each channel of data. Referring toFIG. 18A, it may be seen that there is high powerline noise aroundsample number 6000. Referring to FIG. 18B, it may be seen that thepowerline noise is still high; in some embodiments, this may result inthis interval being flagged as having severe contact issues due to achange in relative R-wave peak energy from one interval to another thatis greater than the threshold value discussed above with reference tostep 1310 of exemplary method 300. Referring to FIG. 18C, it may be seenthat there is high powerline noise around sample number 14000. Referringto FIG. 18D, it may be seen that the powerline noise is still high; insome embodiments, this may result in this interval being flagged ashaving severe contact issues due to the signal RMS exceeding thethreshold discussed above with reference to step 1310 of exemplarymethod 1300. Referring to FIG. 18E, it may be seen that there is highpowerline noise throughout the signal. Referring to FIG. 18F, it may beseen that the powerline noise is still high; in some embodiments, thismay result in this interval being flagged as having severe contactissues due to the SNR of this signal failing to meet the threshold SNRdiscussed above with reference to step 1300 of exemplary method 1300.Referring to FIGS. 18G and 18H, it may be seen a clear signal isvisible; in some embodiments, this may result in this interval not beingflagged as having contact issues.

Referring now to FIGS. 19A and 19B, extraction of R-wave peaks inaccordance with step 1315 is shown. It will be apparent to those ofskill in the art that FIGS. 19A and 19B represent R-wave peak extractionfrom a single channel and that, in a practical implementation of themethod 1300 as described above, data sets comparable to those shown inFIGS. 19A and 19B will be present for each channel of data. FIG. 19Ashows filtered data (e.g., as produced by step 1310) prior to theperformance of step 1315. In FIG. 19A, detected peak positions arerepresented by asterisks. FIG. 19B shows data with extracted peaksfollowing the performance of step 1315. In FIG. 19B, peak positions arerepresented by asterisks. It may be seen that, in FIG. 19A, some of thepeak locations indicated by asterisks are not located at the maximalvalue of the peak in the data, and that such positions are correctlyindicated by the asterisks in FIG. 19B.

Referring now to FIGS. 20A-20C, removal of EMG artifacts in accordancewith step 1320 is shown. It will be apparent to those of skill in theart that FIGS. 20A-20C represent removal of EMG artifacts from a singlechannel and that, in a practical implementation of the method 1300 asdescribed above, data sets comparable to those shown in FIGS. 20A-20Cwill be present for each channel of data. FIG. 20A shows exemplaryfiltered data used as in step 1320 (e.g., as produced by step 1310).FIG. 20B shows same filtered data of FIG. 20A and additionally includesa representation of the motion envelope and the inter-peaks absolutesum. In FIG. 20B, peaks suspected to be corrupted are indicated bydiamonds. FIG. 20C shows a corrected signal after EMG artifactcorrection, as produced by step 1320. In FIG. 20C, the suspect peakshave been removed, with corrected peaks shown circled and the originalpeak values shown in a contrasting shade.

Referring now to FIGS. 21A and 21B, removal of baseline artifacts inaccordance with step 1325 is shown. It will be apparent to those ofskill in the art that FIGS. 21A and 21B represent removal of baselineartifacts from a single channel and that, in a practical implementationof the method 1300 as described above, data sets comparable to thoseshown in FIGS. 21A and 21B will be present for each channel of data.FIG. 21A shows exemplary data prior to baseline artifacts removal thatmay be received as input to step 325. In FIG. 21A, a baseline artifactis indicated within a circle. In the data shown in FIG. 21A, thebaseline ratio between the circled area and the rest of the signal isless than 0.8. In some embodiments, a corrected signal is provided bydividing the rest of the signal by this factor. FIG. 21B shows anexemplary corrected signal such as may be produced by step 1325. In FIG.21A, the baseline artifact area is indicated within a circle. It may beseen by comparing FIGS. 21A and 21B that the baseline artifact has beenremoved.

Referring now to FIGS. 22A and 22B, trimming of outliers and gaps inaccordance with step 1330 is shown. It will be apparent to those ofskill in the art that FIGS. 22A and 22B represent trimming of outliersand gaps from a single channel and that, in a practical implementationof the method 1300 as described above, data sets comparable to thoseshown in FIGS. 22A and 22B will be present for each channel of data.FIG. 22A shows exemplary data that may be received as input to step1330. It may be seen that the input data includes an outlier near sample450, indicated in FIG. 22A with a diamond. FIG. 22B shows the exemplarydata of FIG. 22A after the performance of step 1330 to remove outliersas described above. It may be seen that the outlier shown in FIG. 22Ahas been removed.

Referring now to FIGS. 23A and 23B, interpolation and extraction of anR-wave peak signal in accordance with step 1330 is shown. It will beapparent to those of skill in the art that FIGS. 23A and 23B representextraction of an R-wave peak signal from a single channel and that, in apractical implementation of the method 1300 as described above, datasets comparable to those shown in FIGS. 23A and 23B will be present foreach channel of data. FIG. 23A shows an exemplary R-wave peaks signalthat may be provided as output from step 1330 and received as input tostep 1335. FIG. 23B shows an exemplary clean interpolated R-wave signalthat may be produced by the performance of step 1335.

Referring now to FIGS. 24A and 24B, channel selection in accordance withstep 1335 is shown. In the exemplary data set shown in FIGS. 24A and24B, channels 3 and 8 were found to be ineligible for channel selectiondue to the presence of contact issues in more than 10% of timeintervals. Accordingly, in FIGS. 24A and 24B, only exemplary channels 1,2, 4, 5, 6, and 7 are shown. Independent channel pairs of the data shownin FIG. 24A with corresponding Kendall correlation values are shown inthe table below:

1^(st) 2^(nd) Kendall correlation Channel Channel value 1 5 0.01 1 60.49 1 7 0.51 2 5 0.08 2 6 0.39 2 7 0.58 4 5 −0.16 4 6 0.38 4 7 0.58

It may be seen from the above table that the group consisting ofchannels 1, 2, 4, and 7 demonstrates moderate correlation (e.g.,correlation greater than 0.5 but less than 0.7). Accordingly, channels1, 2, 4 and 7 are selected in step 1340. FIG. 24B shows an exemplarydata set output by step 1340 including selected channels 1, 2, 4, and 7.

Referring now to FIGS. 25A and 25B, calculation of an EUM signal basedon selected channels in accordance with step 1345 is shown. Channel datashown in FIG. 24B is received as input to step 1345 in order to produceoutput data shown in FIGS. 25A-25B. Referring to FIG. 25A, this figureshows an 80^(th) percentile signal extracted from the signals shown inFIG. 24B. FIG. 25B shows a corrected signal obtained by applyingwandering baseline removal to the signal shown in FIG. 25A.

Referring now to FIG. 26, calculation of a normalized EUM signal inaccordance with step 1350 is shown. Corrected data as produced by step1345 and as shown in FIG. 25B is received as input to step 1350 in orderto produce a normalized EUM signal as shown in FIG. 26. FIG. 26 shows anormalized signal obtained by normalizing the signal shown in FIG. 25Band setting the baseline value to 30 visualization voltage units. It maybe seen in FIG. 26 that three weak peaks are present in the signal.

Referring now to FIGS. 27A-27C, sharpening of an EUM signal inaccordance with step 1355 is shown. An exemplary normalized signal asproduced by step 1350, such as the exemplary normalized signal shown inFIG. 26 is received as input to step 1355 in order to produce asharpened EUM signal. FIG. 27A shows an exemplary normalized EUM signalas produced by step 1350. FIG. 27B shows an exemplary enhancement maskgenerated in accordance with step 1355. FIG. 27C shows an exemplarysharpened EUM signal produced by adding the normalized EUM signal ofFIG. 27A to the mask of FIG. 27B.

Referring now to FIG. 28, post-processing of an EUM signal in accordancewith step 1360 is shown. The sharpened EUM signal as produced by step1355 is received as input to step 1360 in order to produce apost-processed EUM signal. FIG. 28 shows an exemplary post-processed EUMsignal after removing a wandering baseline as described above withreference to step 1360. It may be seen that the three weak peaks shownin FIG. 26 are more clearly visible in FIG. 28 following the sharpeningof step 1355 and the post-processing of step 1360.

Referring now to FIG. 29 a tocograph signal corresponding to theexemplary EUM signal of FIG. 28 is shown. As previously noted, theexemplary EUM signal of FIG. 28 is produced in accordance with themethod 1300. The representative tocograph signal of FIG. 29 was capturedfor the same subject during the same time interval as the data used togenerate the exemplary EUM signal of FIG. 28. It may be seen that FIGS.28 and 29 are substantial matches for one another and include the samethree peaks as one another.

As discussed herein, a technical problem in the field of maternal/fetalcare is that existing solutions for monitoring uterine activity (e.g.,contractions) through the use of a tocodynamometer and an ultrasoundtransducer require an expectant mother to wear uncomfortable sensors,and can produce unreliable data when worn by obese expectant mothers(e.g., the sensors may not have sufficient sensitivity to produce usabledata). As further discussed herein, the exemplary embodiments present atechnical solution to this technical problem through the analysis ofdata that can be obtained by bio-potential sensors (e.g., electrodes)integrated into a comfortably wearable device to produce a signal thatcan monitor uterine activity. A further technical problem in the fieldof maternal/fetal care is that existing solutions for analysis based ondata that can be obtained by bio-potential sensors (e.g., electrodes)are limited to analyzing such signals to extract cardiac data. Asdiscussed herein, the exemplary embodiments present a technical solutionto this technical problem through the analysis of bio-potential data toproduce a signal that can monitor uterine activity (e.g., contractions).

Publications cited throughout this document are hereby incorporated byreference in their entirety. Although the various aspects of theinvention have been illustrated above by reference to examples andembodiments, it will be appreciated that the scope of the invention isdefined not by the foregoing description but by the following claimsproperly construed under principles of patent law. Further, manymodifications may become apparent to those of ordinary skill in the art,including that various embodiments of the inventive methodologies, theinventive systems, and the inventive devices described herein can beutilized in any combination with each other. Further still, the varioussteps may be carried out in any desired order (and any desired steps maybe added and/or any undesired steps in a particular embodiment may beeliminated).

What is claimed is:
 1. A computer-implemented method, comprising:receiving, by at least one computer processor, a plurality of rawbio-potential inputs, wherein each of the raw bio-potential inputs beingreceived from a corresponding one of a plurality of electrodes, whereineach of the plurality of electrodes is positioned so as to measure arespective one of the raw bio-potential inputs of a pregnant humansubject; generating, by the at least one computer processor, a pluralityof signal channels from the plurality of raw-bio-potential inputs,wherein the plurality of signal channels comprises at least three signalchannels; pre-processing, by the at least one computer processor,respective signal channel data of each of the signal channels to producea plurality of pre-processed signal channels, wherein each of thepre-processed signal channels comprises respective pre-processed signalchannel data; extracting, by the at least one computer processor, arespective plurality of R-wave peaks from the pre-processed signalchannel data of each of the pre-processed signal channels to produce aplurality of R-wave peak data sets, wherein each of the R-wave peak datasets comprises a respective plurality of R-wave peaks; removing, by theat least one computer processor, from the plurality of R-wave peak datasets, at least one of: (a) at least one signal artifact or (b) at leastone outlier data point, wherein the at least one signal artifact is oneof an electromyography artifact or a baseline artifact; replacing, bythe at least one computer processor, the at least one signal artifact,the at least one outlier data point, or both, with at least onestatistical value determined based on a corresponding one of the R-wavepeak data sets from which the at least one signal artifact, the at leastone outlier data point, or both was removed; generating, by the at leastone computer processor, a respective R-wave signal data set for arespective R-wave signal channel at a predetermined sampling rate basedon each respective R-wave peak data set to produce a plurality of R-wavesignal channels; selecting, by the at least one computer processor, atleast one first selected R-wave signal channel and at least one secondselected R-wave signal channel from the plurality of R-wave channelsbased on at least one correlation between (a) the respective R-wavesignal data set of at least one first particular R-wave signal channeland (b) the respective R-wave signal data set of at least one secondparticular R-wave signal channel; generating, by the at least onecomputer processor, electrical uterine monitoring data representative ofan electrical uterine monitoring signal based on at least the respectiveR-wave signal data set of the first selected R-wave signal channel andthe respective R-wave signal data set of the second selected R-wavesignal channel.
 2. The computer-implemented method of claim 1, furthercomprising: sharpening, by the at least one computer processor, theelectrical uterine monitoring data to produce a sharpened electricaluterine monitoring signal.
 3. The computer-implemented method of claim2, wherein the sharpening step is omitted if the electrical uterinemonitoring data is calculated based on a selected one of the electricaluterine monitoring signal channels that is a corrupted electricaluterine signal monitoring channel.
 4. The computer-implemented method ofclaim 2, further comprising: post-processing the sharpened electricalmonitoring signal data to produce a post-processed electrical uterinemonitoring signal.
 5. The computer-implemented method of claim 2,wherein the sharpening step comprises: identifying a set of peaks in theelectrical uterine monitoring signal data; determining a prominence ofeach of the peaks; removing, from the set of peaks, peaks having aprominence that is less than at least one threshold prominence value;calculating a mask based on remaining peaks of the set of peaks;smoothing the mask based on a moving average window to produce asmoothed mask; and adding the smoothed mask to the electrical uterinemonitoring signal data to produce the sharpened electrical uterinemonitoring signal data.
 6. The computer-implemented method of claim 5,wherein the at least one threshold prominence value includes at leastone threshold prominence value selected from the group consisting of anabsolute prominence value and a relative prominence value calculatedbased on a maximal prominence of the peaks in the set of peaks.
 7. Thecomputer-implemented method of claim 5, wherein the mask includes zerovalues outside areas of the remaining peaks and nonzero values insideareas of the remaining peaks, wherein the nonzero values are calculatedbased on a Gaussian function.
 8. The computer-implemented method ofclaim 1, wherein the at least one filtering step of the pre-processingstep includes applying at least one filter selected from the groupconsisting of a DC removal filter, a powerline filter, and a high passfilter.
 9. The computer-implemented method of claim 1, wherein theextracting step comprises: receiving a set of maternal ECG peaks for thepregnant human subject; and identifying R-wave peaks in each of thepre-processed signal channels within a predetermined time window beforeand after each of the maternal ECG peaks in the set of maternal ECGpeaks as the maximum absolute value in each of the pre-processed signalchannels within the predetermined time window.
 10. Thecomputer-implemented method of claim 1, wherein the step of removing atleast one of a signal artifact or an outlier data point comprisesremoving at least one electromyography artifact by a process comprising:identifying at least one corrupted peak in one of the plurality ofR-wave peaks data sets based on the at least one corrupted peak havingan inter-peaks root mean square value that is greater than a threshold;and replacing the corrupted peak with a median value, wherein the medianvalue is either a local median or a global median.
 11. Thecomputer-implemented method of claim 1, wherein the step of removing atleast one of a signal artifact or an outlier data point comprisesremoving at least one baseline artifact by a process comprising:identifying a change point in R-wave peaks in one of the plurality ofR-wave peaks data sets; subdividing the one of the plurality of R-wavepeaks data sets into a first portion located prior to the change pointand a second portion located subsequent to the change point; determininga first root-mean-square value for the first portion; determining asecond root-mean-square value for the second portion; determining anequalization factor based on the first root-mean-square value and thesecond root-mean-square value; and modifying the first portion bymultiplying R-wave peaks in the first portion by the equalizationfactor.
 12. The computer-implemented method of claim 1, wherein the stepof removing at least one of a signal artifact or an outlier pointcomprises removing at least one outlier in accordance with a Grubbs testfor outliers.
 13. The computer-implemented method of claim 1, whereinthe step of generating a respective R-wave data set based on eachrespective R-wave peak data set comprises interpolating between theR-wave peaks of each respective R-wave peak data set, and wherein theinterpolating between the R-wave peaks comprises interpolating using aninterpolation algorithm that is selected from the group consisting of acubic spline interpolation algorithm and a shape-preserving piecewisecubic interpolation algorithm.
 14. The computer-implemented method ofclaim 1, wherein the step of selecting at least one first one of theR-wave signal channels and at least one second one of the R-wave signalchannels comprises: selecting candidate R-wave signal channels from theR-wave signal channels based on a percentage of prior intervals in whicheach of the R-wave signal channels experienced contact issues; groupingthe selected candidate R-wave signal channels into a plurality ofcouples, wherein each of the couples includes two of the selectedcandidate R-wave channels that are independent from one another;calculating a correlation value of each of the couples; and selecting,as the selected at least one first one of the R-wave signal channels andthe selected at least one second one of the R-wave signal channels, thecandidate R-wave signal channels of at least one of the couples based onthe at least one of the couples having a correlation value that exceedsa threshold correlation value.
 15. The computer-implemented method ofclaim 1, wherein the step of calculating the electrical uterinemonitoring signal comprises calculating a signal that is a predeterminedpercentile of the selected at least one first one of the R-wave signalchannels and the selected at least one second one of the R-wave signalchannels.
 16. The computer-implemented method of claim 15, wherein thepredetermined percentile is an 80th percentile.
 17. Thecomputer-implemented method of claim 1, wherein the statistical value isone of a local median, a global median, or a mean.